Technologies to support self-directed learning through social interaction
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This talk will describe underlying principles, design, and experience gained with ProSolo, a platform that supports personalized, competency-based learning through social interaction. Traditional educational models are primarily focused on classroom education and training typically associated with the notion of credit hours as the (only) route towards formal credentials. This limits opportunities for creating personalized learning pathways in the changing educational context. ProSolo provide users with the ability to unbundle education programs, courses, and units into discrete yet inter-related competencies, allowing learners to construct their education pathway in a manner that better reflects their interests and future career motivations and requirements. ProSolo is developed with the intention of providing learners with opportunities to customize, modify, and personalize their self-directed learning journey. ProSolo supports the development of skills for self-directed learning by allowing learners to control the planning, learning, and presentation of outcomes associated with their learning. To support learners with different levels of prior knowledge, study skills, and cultural backgrounds, ProSolo offers features for supporting self-directed learning through three types of scaffolds, including instructional, social, and technological. Learning in ProSolo occurs within a socially rich environment that aggregates learners’ information created and shared in their existing online spaces. ProSolo makes use of learning analytics to empower learners and instructors in this new model of education. ProSolo was used in the Data, Learning, and Analytics MOOC and is currently being piloted at several university sites.
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Technologies to support self-directed learning through social interaction
- 1. Technologies to support self-directed learning through social interaction Dragan Gašević @dgasevic March 15, 2016 Digital Learning Week 2016 UniSA, Adelaide, SA, Australia Joint work with Zoran Jeremic, Nikola Milikic, George Siemens, and Shane Dawson
- 2. Educational landscape today “Non-traditional” students Long time to complete degrees Large classrooms Long waiting lists
- 3. Confirmation of different needs MOOCs
- 4. Learning does not i) happen only in school and ii) end with graduation
- 5. Departing from credit hours & performance focus
- 6. Mastery learning through competency focus
- 9. Learning Personalization The Learner… Barbara Bray & Kathleen McClaskey, Personalization v Differentiation v Individualization Chart (v3) (2013) , http://www.personalizelearning.com/2013/03/new-personalization-vs-differentiation.html
- 10. The Learner… Learning Personalization Barbara Bray & Kathleen McClaskey, Personalization v Differentiation v Individualization Chart (v3) (2013) , http://www.personalizelearning.com/2013/03/new-personalization-vs-differentiation.html
- 11. Social interaction in learning
- 13. How to tie all these different pressing concerns together?
- 16. Self-directed learning with instructional, social, and technological scaffolding
- 17. Learners construct knowledge Learners are agents Winne, P. H. (2006). How software technologies can improve research on learning and bolster school reform. Educational Psychologist, 41(1), 5–17.
- 18. Feedback, especially process and formative, is a must Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112.
- 19. Competency is a social object
- 20. Learners are unique and so are their profiles
- 21. Learning does not happen in a silo
- 22. Learning is credentialed based on evidence produced
- 23. Institutions learn too (as learning organizations)
- 25. Tenologies builds on Existing learning technologies (LTI), social media, learning analytics, machine learning, information retrieval, recommender systems,…
- 28. Design-based approach iterative with interventions
- 29. Siadaty, M., Gašević, D., & Hatala, M. (2016). Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning. Computers in Human Behavior, 59, 469-482. Effects of technological interventions
- 53. Adjustment to the new approach
- 54. External locus of control
- 55. Change of discourse needed! From adaptive learning to adaptive learners
- 56. Challenge Metacognitive skills Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-Regulated Learning: Beliefs, Techniques, and Illusions. Annual Review of Psychology, 64, 417-444. doi:10.1146/annurev-psych-113011-143823
- 57. Instructional design as the most active ingredient Clark, R. E. (2009). Translating research into new instructional technologies for higher education: The active ingredient process. Journal of Computing in Higher Education, 21(1), 4-18.
- 58. Central role of instructors empowered with technology Learning is not algorithmically driven!
- 59. Credentialing as a big opportunity (if done right!)
- 61. Restructuring exiting educational resources around competencies
- 62. Content frameworks across different disciplines
Editor's Notes
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- Results of this study indicated that the instructors who used Signals perceived this technology primarily as away of transmitting summative feedback multiple times in a semester. With this technology, instructors aimed to inform students where they are standing early in the semester. Results indicated that student success was associated with instructional rather than motivational feedback, and type of rather than frequency of summative and formative feedback. Implications of these findings are discussed below. ► Study 1 examined how instructors define the content and nature of feedback in Signals with content analysis. ► Although formative feedback yields better learning experiences and outcomes, instructors focused on summative feedback. ► Study 2 examined the content and nature of feedback students received via Signals messages and their relation with success. ► Instructors should focus on type of rather than frequency of feedback, and send explicit and instructional feedback. ► Discrepancy between perception and application suggests a feedback message guideline or an inclusive message library. Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education,57(4), 2414-2422.
- Results of this study indicated that the instructors who used Signals perceived this technology primarily as away of transmitting summative feedback multiple times in a semester. With this technology, instructors aimed to inform students where they are standing early in the semester. Results indicated that student success was associated with instructional rather than motivational feedback, and type of rather than frequency of summative and formative feedback. Implications of these findings are discussed below. ► Study 1 examined how instructors define the content and nature of feedback in Signals with content analysis. ► Although formative feedback yields better learning experiences and outcomes, instructors focused on summative feedback. ► Study 2 examined the content and nature of feedback students received via Signals messages and their relation with success. ► Instructors should focus on type of rather than frequency of feedback, and send explicit and instructional feedback. ► Discrepancy between perception and application suggests a feedback message guideline or an inclusive message library. Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education,57(4), 2414-2422.
- understanding and documenting what learners know, regardless of where learning happens, is essential for creating adaptive learning opportunities;
- understanding and documenting what learners know, regardless of where learning happens, is essential for creating adaptive learning opportunities;
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- Learners develop their competencies by harnessing the opportunities offered by social media and connections learners create while learning, working, or playing
- understanding and documenting what learners know, regardless of where learning happens, is essential for creating adaptive learning opportunities;
- understanding and documenting what learners know, regardless of where learning happens, is essential for creating adaptive learning opportunities;
- Although learning may occur in informal and authentic contexts, a robust credentialing pipeline is necessary to assess and validate the evidence learners provide in order to demonstrate the achievement of stated competencies.
- understanding and documenting what learners know, regardless of where learning happens, is essential for creating adaptive learning opportunities;
- Students generally have poor self-regulation skills: Weak metacomprehension – assessment of own knowledge – stop learning, when they don’t know enough Confusion of the rate of learning - stop learning, when they don’t know enough Externally-generated self-monitoring prompts – Azevedo Weak metacognitive awareness – inefficient study tactics used